
"""simple docstring"""



from typing import Dict, Optional

import numpy as np

import datasets


a       :						Any       		=		'''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
'''

a       :						Any       		=		'''
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric("mean_iou")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
'''

a       :						Union[str, Any]       		=		'''\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}'''


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A = None       ,   _A = False       ,   )     ->	str:





    """simple docstring"""

    if label_map is not None:
        for old_id, new_id in label_map.items():
            _UpperCAmelCase        = new_id

    # turn into Numpy arrays
    _UpperCAmelCase        = np.array(_A   )
    _UpperCAmelCase        = np.array(_A   )

    if reduce_labels:
        _UpperCAmelCase        = 2_5_5
        _UpperCAmelCase        = label - 1
        _UpperCAmelCase        = 2_5_5

    _UpperCAmelCase        = label != ignore_index
    _UpperCAmelCase        = np.not_equal(_A       ,   _A   )
    _UpperCAmelCase        = pred_label[mask]
    _UpperCAmelCase        = np.array(_A   )[mask]

    _UpperCAmelCase        = pred_label[pred_label == label]

    _UpperCAmelCase        = np.histogram(_A       ,   bins=_A       ,   range=(0, num_labels - 1)   )[0]
    _UpperCAmelCase        = np.histogram(_A       ,   bins=_A       ,   range=(0, num_labels - 1)   )[0]
    _UpperCAmelCase        = np.histogram(_A       ,   bins=_A       ,   range=(0, num_labels - 1)   )[0]

    _UpperCAmelCase        = area_pred_label + area_label - area_intersect

    return area_intersect, area_union, area_pred_label, area_label


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A = None       ,   _A = False       ,   )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = np.zeros((num_labels,)       ,   dtype=np.floataa   )
    _UpperCAmelCase        = np.zeros((num_labels,)       ,   dtype=np.floataa   )
    _UpperCAmelCase        = np.zeros((num_labels,)       ,   dtype=np.floataa   )
    _UpperCAmelCase        = np.zeros((num_labels,)       ,   dtype=np.floataa   )
    for result, gt_seg_map in zip(_A       ,   _A   ):
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = intersect_and_union(
            _A       ,   _A       ,   _A       ,   _A       ,   _A       ,   _A   )
        total_area_intersect += area_intersect
        total_area_union += area_union
        total_area_pred_label += area_pred_label
        total_area_label += area_label
    return total_area_intersect, total_area_union, total_area_pred_label, total_area_label


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A = None       ,   _A = None       ,   _A = False       ,   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = total_intersect_and_union(
        _A       ,   _A       ,   _A       ,   _A       ,   _A       ,   _A   )

    # compute metrics
    _UpperCAmelCase        = {}

    _UpperCAmelCase        = total_area_intersect.sum() / total_area_label.sum()
    _UpperCAmelCase        = total_area_intersect / total_area_union
    _UpperCAmelCase        = total_area_intersect / total_area_label

    _UpperCAmelCase        = np.nanmean(_A   )
    _UpperCAmelCase        = np.nanmean(_A   )
    _UpperCAmelCase        = all_acc
    _UpperCAmelCase        = iou
    _UpperCAmelCase        = acc

    if nan_to_num is not None:
        _UpperCAmelCase        = {metric: np.nan_to_num(_A       ,   nan=_A   ) for metric, metric_value in metrics.items()}

    return metrics







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,    _KWARGS_DESCRIPTION						)
class 		a_    (			datasets.Metric						):







    def   _snake_case       (      self      :							str  )		->List[Any]:






        '''simple docstring'''






        return datasets.MetricInfo(
            description=_DESCRIPTION  ,       citation=_CITATION  ,       inputs_description=_KWARGS_DESCRIPTION  ,       features=datasets.Features(
                # 1st Seq - height dim, 2nd - width dim
                {
                    """predictions""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16"""  )  )  ),
                    """references""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16"""  )  )  ),
                }  )  ,       reference_urls=[
                """https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"""
            ]  ,       )







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							bool  ,       __UpperCamelCase      :							Optional[int] = None  ,       __UpperCamelCase      :							Optional[Dict[int, int]] = None  ,       __UpperCamelCase      :							bool = False  ,       )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = mean_iou(
            results=__UpperCamelCase  ,       gt_seg_maps=__UpperCamelCase  ,       num_labels=__UpperCamelCase  ,       ignore_index=__UpperCamelCase  ,       nan_to_num=__UpperCamelCase  ,       label_map=__UpperCamelCase  ,       reduce_labels=__UpperCamelCase  ,       )
        return iou_result

"""simple docstring"""



import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer




class 		a_    :







    def __init__(      self      :							Any  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							Dict=13  ,       __UpperCamelCase      :							Tuple=7  ,       __UpperCamelCase      :							List[Any]=True  ,       __UpperCamelCase      :							List[str]=True  ,       __UpperCamelCase      :							Union[str, Any]=False  ,       __UpperCamelCase      :							List[str]=True  ,       __UpperCamelCase      :							str=99  ,       __UpperCamelCase      :							Optional[Any]=32  ,       __UpperCamelCase      :							Union[str, Any]=5  ,       __UpperCamelCase      :							Tuple=4  ,       __UpperCamelCase      :							Any=37  ,       __UpperCamelCase      :							str="gelu"  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							int=0.1  ,       __UpperCamelCase      :							List[str]=5_12  ,       __UpperCamelCase      :							Union[str, Any]=16  ,       __UpperCamelCase      :							Union[str, Any]=2  ,       __UpperCamelCase      :							Union[str, Any]=0.0_2  ,       __UpperCamelCase      :							List[str]=3  ,       __UpperCamelCase      :							Optional[Any]=4  ,       __UpperCamelCase      :							str=None  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = parent
        _UpperCAmelCase        = batch_size
        _UpperCAmelCase        = seq_length
        _UpperCAmelCase        = is_training
        _UpperCAmelCase        = use_input_mask
        _UpperCAmelCase        = use_token_type_ids
        _UpperCAmelCase        = use_labels
        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = type_sequence_label_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = num_labels
        _UpperCAmelCase        = num_choices
        _UpperCAmelCase        = scope







    def   _snake_case       (      self      :							Optional[int]  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.vocab_size  )

        _UpperCAmelCase        = None
        if self.use_input_mask:
            _UpperCAmelCase        = random_attention_mask([self.batch_size, self.seq_length]  )

        _UpperCAmelCase        = None
        if self.use_token_type_ids:
            _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.type_vocab_size  )

        _UpperCAmelCase        = None
        _UpperCAmelCase        = None
        _UpperCAmelCase        = None
        if self.use_labels:
            _UpperCAmelCase        = ids_tensor([self.batch_size]  ,       self.type_sequence_label_size  )
            _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.num_labels  )
            _UpperCAmelCase        = ids_tensor([self.batch_size]  ,       self.num_choices  )

        _UpperCAmelCase        = self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







    def   _snake_case       (      self      :							str  )		->Any:






        '''simple docstring'''






        return LlamaConfig(
            vocab_size=self.vocab_size  ,       hidden_size=self.hidden_size  ,       num_hidden_layers=self.num_hidden_layers  ,       num_attention_heads=self.num_attention_heads  ,       intermediate_size=self.intermediate_size  ,       hidden_act=self.hidden_act  ,       hidden_dropout_prob=self.hidden_dropout_prob  ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,       max_position_embeddings=self.max_position_embeddings  ,       type_vocab_size=self.type_vocab_size  ,       is_decoder=__UpperCamelCase  ,       initializer_range=self.initializer_range  ,       )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaModel(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  )
        _UpperCAmelCase        = model(__UpperCamelCase  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.seq_length, self.hidden_size)  )







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Union[str, Any]  ,       )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = True
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       )
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       )
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.seq_length, self.hidden_size)  )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							int  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaForCausalLM(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.seq_length, self.vocab_size)  )







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Dict  ,       )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = True
        _UpperCAmelCase        = True
        _UpperCAmelCase        = LlamaForCausalLM(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()

        # first forward pass
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       use_cache=__UpperCamelCase  ,       )
        _UpperCAmelCase        = outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        _UpperCAmelCase        = ids_tensor((self.batch_size, 3)  ,       config.vocab_size  )
        _UpperCAmelCase        = ids_tensor((self.batch_size, 3)  ,       vocab_size=2  )

        # append to next input_ids and
        _UpperCAmelCase        = torch.cat([input_ids, next_tokens]  ,       dim=-1  )
        _UpperCAmelCase        = torch.cat([input_mask, next_mask]  ,       dim=-1  )

        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       output_hidden_states=__UpperCamelCase  ,       )["""hidden_states"""][0]
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       past_key_values=__UpperCamelCase  ,       output_hidden_states=__UpperCamelCase  ,       )["""hidden_states"""][0]

        # select random slice
        _UpperCAmelCase        = ids_tensor((1,)  ,       output_from_past.shape[-1]  ).item()
        _UpperCAmelCase        = output_from_no_past[:, -3:, random_slice_idx].detach()
        _UpperCAmelCase        = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-3  )  )







    def   _snake_case       (      self      :							Tuple  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = self.prepare_config_and_inputs()
        (
            (
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,
        )        = config_and_inputs
        _UpperCAmelCase        = {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_torch
class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					Optional[Any]			     =					(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
    a   :					Any			     =					(LlamaForCausalLM,) if is_torch_available() else ()
    a   :					Union[str, Any]			     =					(
        {
            'feature-extraction': LlamaModel,
            'text-classification': LlamaForSequenceClassification,
            'text-generation': LlamaForCausalLM,
            'zero-shot': LlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a   :					str			     =					False
    a   :					List[str]			     =					False







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaModelTester(self  )
        _UpperCAmelCase        = ConfigTester(self  ,       config_class=__UpperCamelCase  ,       hidden_size=37  )







    def   _snake_case       (      self      :							Optional[int]  )		->Optional[Any]:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    def   _snake_case       (      self      :							Dict  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase  )







    def   _snake_case       (      self      :							int  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            _UpperCAmelCase        = type
            self.model_tester.create_and_check_model(*__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  )		->int:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor([self.model_tester.batch_size]  ,       self.model_tester.type_sequence_label_size  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    def   _snake_case       (      self      :							Dict  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = """single_label_classification"""
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor([self.model_tester.batch_size]  ,       self.model_tester.type_sequence_label_size  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    def   _snake_case       (      self      :							List[str]  )		->str:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = """multi_label_classification"""
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor(
            [self.model_tester.batch_size, config.num_labels]  ,       self.model_tester.type_sequence_label_size  ).to(torch.float  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    @unittest.skip("""LLaMA buffers include complex numbers, which breaks this test"""  )
    def   _snake_case       (      self      :							Dict  )		->int:






        '''simple docstring'''






        pass







    @parameterized.expand([("""linear""",), ("""dynamic""",)]  )
    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							Tuple  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = ids_tensor([1, 10]  ,       config.vocab_size  )
        _UpperCAmelCase        = ids_tensor([1, int(config.max_position_embeddings * 1.5  )]  ,       config.vocab_size  )

        set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        original_model.to(__UpperCamelCase  )
        original_model.eval()
        _UpperCAmelCase        = original_model(__UpperCamelCase  ).last_hidden_state
        _UpperCAmelCase        = original_model(__UpperCamelCase  ).last_hidden_state

        set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
        _UpperCAmelCase        = {"""type""": scaling_type, """factor""": 1_0.0}
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        scaled_model.to(__UpperCamelCase  )
        scaled_model.eval()
        _UpperCAmelCase        = scaled_model(__UpperCamelCase  ).last_hidden_state
        _UpperCAmelCase        = scaled_model(__UpperCamelCase  ).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )
        else:
            self.assertFalse(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )







@require_torch
class 		a_    (			unittest.TestCase						):







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							str  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor([input_ids]  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							Optional[int]  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							Dict  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]  )
        # fmt: on
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )







    @unittest.skip(
        """Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"""  )
    @slow
    def   _snake_case       (      self      :							str  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )

        _UpperCAmelCase        = torch.tensor(
            [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]  ,       dtype=torch.floataa  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Model is curently gated"""  )
    @slow
    def   _snake_case       (      self      :							Tuple  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
        _UpperCAmelCase        = """Simply put, the theory of relativity states that """
        _UpperCAmelCase        = LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  )
        _UpperCAmelCase        = tokenizer.encode(__UpperCamelCase  ,       return_tensors="""pt"""  )
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained(
            """meta-llama/Llama-2-13b-chat-hf"""  ,       device_map="""sequential"""  ,       use_safetensors=__UpperCamelCase  )

        # greedy generation outputs
        _UpperCAmelCase        = model.generate(__UpperCamelCase  ,       max_new_tokens=64  ,       top_p=__UpperCamelCase  ,       temperature=1  ,       do_sample=__UpperCamelCase  )
        _UpperCAmelCase        = tokenizer.decode(generated_ids[0]  ,       skip_special_tokens=__UpperCamelCase  )
        self.assertEqual(__UpperCamelCase  ,       __UpperCamelCase  )